Data with Soumya
Data Engineering Mentor
Azure Data EngineeringRoadmap
Learn Azure Data Engineering step-by-step including ADF, Databricks, ADLS, Microsoft Fabric, Synapse, ETL pipelines, and enterprise cloud architectures.
Why Azure Data Engineering is Important
Azure is one of the most widely used cloud platforms for enterprise Data Engineering and analytics workflows. Modern organizations use Azure services and Microsoft Fabric to build scalable cloud-native data platforms.
Strong Azure Data Engineering skills help you build cloud ETL pipelines, process massive datasets, implement lakehouse architectures, and work with enterprise analytics ecosystems.
Structured AzureLearning Path
Follow this step-by-step roadmap to build strong Azure Data Engineering foundations and real-world cloud workflows.
Phase 1 — Cloud & Azure Fundamentals
Cloud Basics
- ✓What is Cloud Computing
- ✓IaaS vs PaaS vs SaaS
- ✓Cloud architecture basics
- ✓Scalability concepts
Azure Fundamentals
- ✓Azure portal overview
- ✓Resource groups
- ✓Azure subscriptions
- ✓Storage accounts
Phase 2 — Azure Storage & ADLS
Azure Blob Storage
- ✓Blob storage concepts
- ✓Storage accounts
- ✓Containers & blobs
- ✓Storage access tiers
Azure Data Lake Storage (ADLS)
- ✓ADLS Gen2
- ✓Hierarchical namespace
- ✓Folder structures
- ✓Data lake best practices
Storage Security & Access
- ✓Access control basics
- ✓Shared access signatures
- ✓RBAC concepts
- ✓Data organization strategies
Phase 3 — Azure Data Factory (ADF)
ADF Fundamentals
- ✓ADF architecture
- ✓Linked services
- ✓Datasets
- ✓Integration runtime
Pipeline Development
- ✓Copy activity
- ✓Pipeline orchestration
- ✓Triggers & scheduling
- ✓Parameterization
Advanced ADF Concepts
- ✓Dynamic pipelines
- ✓Incremental loading
- ✓Error handling
- ✓Monitoring & debugging
Phase 4 — Azure Databricks & PySpark
Databricks Fundamentals
- ✓Databricks workspace
- ✓Clusters
- ✓Notebooks
- ✓Workspace architecture
PySpark in Databricks
- ✓DataFrame operations
- ✓Transformations
- ✓Spark SQL
- ✓Joins & aggregations
Optimization Concepts
- ✓Caching
- ✓Partitioning
- ✓Performance tuning
- ✓Delta Lake basics
Phase 5 — Azure Synapse Analytics
Synapse Fundamentals
- ✓Azure Synapse overview
- ✓Dedicated vs serverless pools
- ✓Analytics architecture
- ✓Workspace concepts
Data Warehousing
- ✓Warehouse concepts
- ✓Data marts
- ✓ETL vs ELT
- ✓Analytical workflows
Phase 6 — Microsoft Fabric & Lakehouse
Microsoft Fabric Fundamentals
- ✓What is Microsoft Fabric
- ✓Fabric ecosystem overview
- ✓Unified analytics platform
- ✓Modern analytics architecture
Lakehouse & OneLake
- ✓Lakehouse concepts
- ✓OneLake
- ✓Medallion architecture
- ✓Data organization strategies
Fabric Components
- ✓Fabric pipelines
- ✓Fabric notebooks
- ✓Fabric Data Warehouse
- ✓Power BI integration
Phase 7 — Real-World Azure Data Engineering
Pipeline Architecture
- ✓End-to-end ETL pipelines
- ✓Raw to curated architecture
- ✓Data validation
- ✓Logging & monitoring
Enterprise Concepts
- ✓CI/CD basics
- ✓Security & RBAC
- ✓Cost optimization
- ✓Production workflows
Project Building
- ✓ADF + Databricks integration
- ✓ADLS pipelines
- ✓Fabric analytics projects
- ✓Real-world implementation practice
How to Practice Effectively
Learning Azure Data Engineering requires hands-on implementation with cloud services, distributed systems, storage architectures, and enterprise analytics workflows.
Daily Practice
- • Practice building ADF pipelines regularly
- • Work with ADLS & Blob Storage structures
- • Practice Databricks transformations
- • Explore Microsoft Fabric workflows
- • Understand cloud architecture deeply
Build Projects
- • Build end-to-end ETL pipelines
- • Create ADLS + Databricks workflows
- • Implement Fabric analytics projects
- • Build lakehouse architectures
- • Practice real-world cloud implementations
Need PersonalizedAzure Guidance?
Get mentorship, roadmap guidance, interview preparation, and practical learning support tailored to your Data Engineering journey.